## Overview {.tabset .tabset-pills}
The following report examines….A brief summary (3 - 4 sentences) of the dataset, and what is included in this report
A map of the fish ladder location (you can make this in R on your own, or include an existing map appropriately licensed, with attribution)
A professionally formatted data citation
knitr::opts_chunk$set(echo = TRUE)
# attach packages
library(tidyverse)
library(here)
library(lubridate)
library(tsibble)
library(feasts)
library(slider)
library(janitor)
library(patchwork)
library(gghighlight)
library(plotly)
Reading in the data
# reading in and preparing the data
fish <- read_csv(here("data", "willamette_fish_passage.csv")) %>%
clean_names() %>%
mutate(date = lubridate::mdy(date)) %>% # turning the date into mdy format with lubridate
as_tsibble(key = NULL, index = date) %>% # changing the format to a tsibble for use in time series
select(date, coho, jack_coho, steelhead) # selecting for fish species of interest
## Rows: 3652 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Project, Date
## dbl (6): Chinook, Jack Chinook, Steelhead, Coho, Jack Coho, TempC
## lgl (8): Chinook Run, Wild Steelhead, Sockeye, Shad, Lamprey, Bull Trout, Ch...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# changing all NA values to 0
fish[is.na(fish)] <- 0
# creating a longer df with single observations for each of the fish species of interest
fish_longer <- fish %>%
pivot_longer(cols = 2:4, names_to = "species", values_to = "count") %>% # selecting for fish species of interest
mutate(species = case_when(
species == "coho" ~ "Coho",
species == "jack_coho" ~ "Jack Coho",
species == "steelhead" ~ "Steelhead"))
# changing all NA values to 0
fish[is.na(fish)] <- 0
ggplot(data = fish_longer, aes(x = date,
y = count,
color = species)) +
geom_line (size = 0.4) +
theme_minimal() +
theme(legend.position = c(.5, .8),
legend.title = element_blank(),
legend.text = element_text(
size = 12
)) +
scale_color_manual(values = c("firebrick3", "goldenrod", "darkgreen"))
We need to ask some big picture questions at this point, like:
# season plot for each fish species
fish_longer %>%
gg_season(y = count) +
facet_wrap( ~ species)
Add 2 - 3 bullet points summarizing the major trends you see in the seasonplots.
fish_annual <- fish_longer %>%
index_by(year = ~year(.)) %>%
group_by(species) %>%
summarize(yearly_counts = sum(count))
ggplot(data = fish_annual, aes(x = year, y = yearly_counts, color = species)) +
geom_line()